Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

G3: Genes, Genomes, Genetics

Pichet Grudloyma³  Jill E. Cairns²  Amsal Tarekegne²  Yoseph Beyene²  Albrecht E. Melchinger¹  Babu Raman²  Vanessa S. Windhausen¹  Kassa Semagn²  Jose Crossa²  Jean-Luc Jannink⁴  John M. Hickey²  Mark E. Sorrells⁴  Christian Riedelsheimer¹  Frank Technow¹  Gary N. Atlin² 

[1] Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, GermanyInstitute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, GermanyInstitute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany;International Maize and Wheat Improvement Center (CIMMYT), El Batan 56130, MexicoInternational Maize and Wheat Improvement Center (CIMMYT), El Batan 56130, MexicoInternational Maize and Wheat Improvement Center (CIMMYT), El Batan 56130, Mexico;Nakhon Sawan Field Crops Research Center, Nakhon Sawan 60190, ThailNakhon Sawan Field Crops Research Center, Nakhon Sawan 60190, ThailNakhon Sawan Field Crops Research Center, Nakhon Sawan 60190, Thail;United States Department of Agricultural Research Service and Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853United States Department of Agricultural Research Service and Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853United States Department of Agricultural Research Service and Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853

关键词: GenPred;    shared data resources;    grain yield;    maize;   

DOI  :  10.1534/g3.112.003699

学科分类：生物科学（综合）

来源: Genetics Society of America

【 摘 要 】

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F₂-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F₂-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.

【 授权许可】

Unknown